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Data Analysis for Back Pain Based on the National Population Health Survey

Back pain is an important health and economic problem affecting a significant part of our population. It is of interest to both medical and behavioral professionals concerned with the complex role of the social and psychological factors in the etiology of somatic ailments. Although there has been much written about back injuries in military and industrial settings, little is known about the epidemiological patterns in a general population (Nagi et al., 1973). The objective of this study is to find: a) the major factors connected to back pain, b) whether the general work-stress index is related to back pain, where the general work-stress index is the sum of job stressors including psychological demands, job insecurity, physical exertion, decision latitude and the social support at work, and c) the relationship especially amongst back pain, activity restriction, age, job satisfaction and income. The National Population Health Survey (NPHS) database is used in this project. Some statistical techniques such as logistic regression and log-linear models are used for data analysis. In this project all explanatory variables in logistic regression models are treated as continuous variables; all variables when used in log-linear models are treated as categorical data. Results are compared between these different methods. They are in close agreement with each other. We conclude that age has very high impact on back pain with significance level being lower than 1 %; activity restriction also has a strong relationship with back pain; chronic stress, childhood and adult stressors all have high association with back pain; job stressor and recent life bad events are related fairly to back pain at significant level 5%; and income and job satisfaction do not have direct impact on back pain. Although there is not much that can be done to change the normal aging process of the spinal column, some of the predictors identified such as job stressors are amenable to change. / Thesis / Master of Science (MS)

Identiferoai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/24501
Date11 1900
CreatorsChen, Xiong
ContributorsBalakrishnan, N., Statistics
Source SetsMcMaster University
LanguageEnglish
Detected LanguageEnglish
TypeThesis

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